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Proceedings Paper

Spectroscopic modeling of nitro group in explosives
Author(s): Doris Núñez-Quintero; Samuel P. Hernández-Rivera
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Paper Abstract

Calibration is the process of constructing a mathematical model to relate the output of an instrument to properties of samples. Prediction is the process of using the model to predict properties of a sample given and instrument output. A statistical characterization, based on Discriminant Analysis (DA), of explosive substances allows a characterization and classification of the spectroscopic properties of the effect of nitro group in accordance with the molecular structure. This characterization should help for predicting the nitro group effect in other explosives substances and be a primary actor in sensor design based on IR and Raman Spectroscopies. The goal of this work was to develop a statistical model for the spectroscopic behavior for the nitro group in nitrogen based explosives (nitroexplosives) using DA. The variables used in this analysis were the Raman shift and IR wavenumber (spectral locations) of the symmetric and asymmetric mode of the nitro group. A second group of variables were the absorbance and Raman scattering intensity. The KBr technique was used for running the samples in FTIR. The samples were measured at a 4 cm-1 resolution and 32 scans. Spectra were collected using Bruker OPUS version 4.2 software, in the range of 400-4000 wavenumbers (cm-1). Raman spectra of samples were collected from neat samples deposited on stainless steel slides (for solids) and in melting point capillary tubes. Raman analysis was carried out by using a confocal Renishaw Raman Microspectrometer Model RM2000 equipped with solid state diode laser system emitting at a wavelength of 532 nm as the excitation source. A statistical model using forty five explosives is presented.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470Z (17 April 2006); doi: 10.1117/12.666546
Show Author Affiliations
Doris Núñez-Quintero, Univ. of Puerto Rico Mayagüez (United States)
Samuel P. Hernández-Rivera, Univ. of Puerto Rico Mayagüez (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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